{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e782636b",
   "metadata": {},
   "source": [
    "# NOTE:\n",
    "This is based on the <b><tt>reflec-analysis.ipynb</tt></b>.\n",
    "To basic idea is similar to <b><tt>nite.mac</tt></b> at APS for non-disruptive, continuous data collection without human interference during overnight.\n",
    "\n",
    "SO the steps is as follows:\n",
    "- Initialize the analysis method/model. (universal to all data files)\n",
    "- Speficiy the bounds of parameter space for all. (universal to all data files)\n",
    "- Define Monte-Carlo process to process data file sequentially.\n",
    "- Specifiy the data file collection that will be load and analyzed sequentially.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "610763bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.optimize as opt\n",
    "from scipy.optimize import curve_fit\n",
    "import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import time\n",
    "%config InlineBackend.figure_format='retina'\n",
    "from concurrent.futures import ThreadPoolExecutor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "69d20b62",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rho_sub=0.334 # water or 1mM K \n",
    "# rho_sub=0.3380936 # K2CO3 100 mM\n",
    "rho_sub=0.33440936 # K2CO3 10 mM\n",
    "\n",
    "# NSLS-II 12ID SMI x-ray energy, E_APS\n",
    "E_APS=9.7 # 10 keV x-ray energy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71566d50",
   "metadata": {},
   "source": [
    "## Step 1 : Initialize the methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "24702ab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x-ray energy: 9.7 keV\n",
       "x-ray wavelength : 1.2781443298969073 Å \n",
       "Subphase electron density : 0.33440936 e/Å³ \n",
       "Critical Qz : 0.0218 1/Å\n",
       "Core algorithm : Parratt's recursive method\n",
       "slabs of thickness 1.0 Å to approximate ED profile"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note: set normalize=False will fit non-normalized reflectivity\n",
    "import reflec_calc_lib2 as ref\n",
    "\n",
    "Model =  ref.ReflecModel(9.7,\n",
    "                         rho_sub,\n",
    "                         d_slab=1.0,\n",
    "                         normalize=False)\n",
    "Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b09371d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "fitRef = Model.fitReflec # Working horse of the fitting function\n",
    "fitRF = Model.RF_calc # calculate the Fresnel reflectivity \n",
    "fitRho = Model.renderEDprofile # Generate ED profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "fc20aecd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getChisq(fitfun, param, x, y, yerr):\n",
    "    yfit = fitfun(x, *param)\n",
    "    return np.sqrt(np.sum(((yfit-y)/yerr)**2))/(len(x)-len(param))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e96fc27",
   "metadata": {},
   "source": [
    "## Step 2 : Specify the parameter bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4d67c2e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bound of each parameter\n",
    " \n",
    "I0_min=0.7; I0_max=1.1\n",
    "Qz_offset_min=-0.002; Qz_offset_max=0.002 \n",
    "sig0_min=3; sig0_max=10\n",
    "\n",
    "d1_min=5; d1_max=70  # width of 1st box\n",
    "rho1_min=rho_sub; rho1_max=0.8 # height(electron density) of 1st box\n",
    "sig1_min=1; sig1_max=50 # roughness of the layer-1 bottom interface\n",
    "\n",
    "d2_min=5; d2_max=70 # width of 2nd box\n",
    "rho2_min=rho_sub; rho2_max=0.8 # height(electron density) of 2nd box\n",
    "sig2_min=1; sig2_max=50 # roughness of the layer-1 bottom interface\n",
    "\n",
    "d3_min=5; d3_max=50 # width of 3rd box\n",
    "rho3_min=rho_sub; rho3_max=0.8 # height(electron density) of 3rd box\n",
    "sig3_min=1; sig3_max=50  # roughness of the layer-1 bottom interface\n",
    "\n",
    "# d4_min=10; d4_max=150 # width of 4th box\n",
    "# rho4_min=rho_sub; rho4_max=0.6 # height(electron density) of 3rd box\n",
    "# sig4_min=3; sig4_max=50  # roughness of the layer-1 bottom interface\n",
    "\n",
    "param_lb=np.array([I0_min, Qz_offset_min, sig0_min,\n",
    "          d1_min, rho1_min, sig1_min,\n",
    "          d2_min, rho2_min, sig2_min, \n",
    "          d3_min, rho3_min, sig3_min,\n",
    "          # d4_min, rho4_min, sig4_min,\n",
    "          ])\n",
    "\n",
    "param_ub=np.array([I0_max, Qz_offset_max, sig0_max,\n",
    "          d1_max, rho1_max, sig1_max, \n",
    "          d2_max, rho2_max, sig2_max,\n",
    "          d3_max, rho3_max, sig3_max,\n",
    "          # d4_max, rho4_max, sig4_max,\n",
    "          ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8926e25a-c705-49b8-a89b-488323ebc4ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7222f419",
   "metadata": {},
   "source": [
    "## Step 3: Define a Monte-Carlo simulation process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "12df8de9-10d9-4840-9c2a-843bd36a90d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_combined(x, yn, xfit, yfit, z, rhoRef, base_filename, output_dir):\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
    "\n",
    "    # Plot experimental fit\n",
    "    ax1.plot(x, yn, 'o', markersize=8, label='Experimental Data', color='red', alpha=0.6)\n",
    "    ax1.plot(xfit, yfit, '-', lw=3, label='Fit')\n",
    "    ax1.set_yscale('log')\n",
    "    ax1.set_xlabel(r'$Q_z$', fontsize=20)\n",
    "    ax1.set_ylabel(r'$R/RF$', fontsize=20)\n",
    "    ax1.tick_params(axis='both', labelsize=9)\n",
    "    ax1.set_title('Reflectivity Fitting',fontsize=20)\n",
    "    ax1.legend()\n",
    "\n",
    "    # Plot electron density\n",
    "    ax2.plot(z, rhoRef, '-', lw=3, color='k')\n",
    "    ax2.set_ylim(-0.05, 0.8)\n",
    "    ax2.set_ylabel(r'$\\rho$ $[e/A^3]$', fontsize=20)\n",
    "    ax2.set_xlabel('z [A]', fontsize=20)\n",
    "    ax2.set_title('Electron Density',fontsize=20)\n",
    "\n",
    "    fig.tight_layout()\n",
    "    # Save the combined figure\n",
    "    fig_file = os.path.join(output_dir, f'{base_filename}_combined_fig.png')\n",
    "    fig.savefig(fig_file, dpi=300)\n",
    "    print(f'Saved the figure to: {fig_file}')\n",
    "    plt.show()\n",
    "\n",
    "def monte_carlo_process(data_path, output_dir, filename, loop_number=100):\n",
    "    \"\"\"Monte-Carlo style of data fitting for one data set\"\"\"\n",
    "    # Get x, y, yerr from filename\n",
    "    data = pd.read_csv(data_path, sep='\\s+')\n",
    "    x = data['qz'][2:]\n",
    "    y = data['ref'][2:]\n",
    "    yerr = data['error'][2:]\n",
    "\n",
    "    # Create base output filename\n",
    "    base_filename = os.path.splitext(filename)[0]\n",
    "\n",
    "    # Strip off the .txt extension if present\n",
    "    if base_filename.endswith('.txt'):\n",
    "        base_filename = base_filename[:-4]\n",
    "\n",
    "    # determine the output file name to save parameters\n",
    "    param_file = os.path.join(output_dir, f'{base_filename}_params.csv')\n",
    "\n",
    "    # Loop for random trial of fitting (Monte Carlo part)\n",
    "    chisq_min = 1e6  # a very large number\n",
    "    np.random.seed(1)\n",
    "    record_collection = []\n",
    "    for count in range(loop_number):\n",
    "        print(\"# {} fitting ...\".format(count))\n",
    "\n",
    "        c = np.random.rand(len(param_lb))\n",
    "        p0 = param_lb + (param_ub - param_lb) * c\n",
    "        t0 = time.time()\n",
    "        try:\n",
    "            param_opt, param_cov = curve_fit(fitRef, x, y, p0, sigma=yerr,\n",
    "                                             bounds=(param_lb, param_ub),\n",
    "                                             maxfev=1000)\n",
    "            t1 = time.time()\n",
    "            print('Elapse time :', t1 - t0)\n",
    "\n",
    "            chisq = getChisq(fitRef, param_opt, x, y, yerr)\n",
    "            if chisq <= chisq_min:\n",
    "                chisq_min = chisq\n",
    "                param_best = param_opt\n",
    "\n",
    "            print('chisq for this #{} run is : {}'.format(count, chisq),\n",
    "                  end='  ')\n",
    "            print(\"chisq best now is :\", chisq_min)\n",
    "            print('-' * 20)\n",
    "            record = {'attempt #': count, 'chisq': chisq, 'params': param_opt}\n",
    "            record_collection.append(record)\n",
    "        except RuntimeError as e:\n",
    "            print(\"RuntimeError :\", e)\n",
    "    print(\"Finally, best parameters are :------\\n\", param_best)\n",
    "\n",
    "    df = pd.DataFrame(record_collection)\n",
    "    df.sort_values(by='chisq', inplace=True)\n",
    "    df.reset_index(inplace=True)\n",
    "    df.to_csv(param_file, index=False)\n",
    "    print('Did save the params to :', param_file)\n",
    "\n",
    "    # Additional Plots\n",
    "    xfit = np.linspace(np.min(x), np.max(x), 200)\n",
    "    popt = param_best\n",
    "    xerr = popt[1]\n",
    "    y_RF = fitRF(x - xerr)\n",
    "    y_RF_fit = fitRF(xfit - xerr)\n",
    "    yfit = fitRef(xfit, *popt) / y_RF_fit\n",
    "\n",
    "    yn = y / y_RF\n",
    "    yn_err = yerr / y_RF\n",
    "\n",
    "    # Save DataFrame to a .csv file\n",
    "    df_exp = pd.DataFrame({'x': x, 'yn': yn})\n",
    "    df_fit = pd.DataFrame({'xfit': xfit, 'yfit': yfit})\n",
    "\n",
    "    df_exp_file = os.path.join(output_dir, f'{base_filename}_rrf.csv')\n",
    "    df_fit_file = os.path.join(output_dir, f'{base_filename}_rrf_fit.csv')\n",
    "    df_exp.to_csv(df_exp_file, index=False)\n",
    "    df_fit.to_csv(df_fit_file, index=False)\n",
    "\n",
    "    print(f'Saved experimental data to: {df_exp_file}')\n",
    "    print(f'Saved fit data to: {df_fit_file}')\n",
    "\n",
    "    p = popt\n",
    "    z=np.arange(-300,50,1)\n",
    "    rhoRef = fitRho(z, *p)\n",
    "\n",
    "    # Create a DataFrame from z and rhoRef\n",
    "    df_rho = pd.DataFrame({'z': z, 'rho': rhoRef})\n",
    "\n",
    "    df_rho_file = os.path.join(output_dir, f'{base_filename}_ED.csv')\n",
    "    df_rho.to_csv(df_rho_file, index=False)\n",
    "\n",
    "    print(f'Saved electron density data to: {df_rho_file}')\n",
    "\n",
    "    # Combine and plot experimental fit and electron density\n",
    "    plot_combined(x, yn, xfit, yfit, z, rhoRef, base_filename, output_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19329af3-003d-43ef-8dad-5072428e6fc3",
   "metadata": {},
   "source": [
    "### old code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5393931e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def monte_carlo_process(data_path, output_path, loop_number=100):\n",
    "#     \"\"\"Monte-Carlo style of data fitting for one data set\n",
    "#     \"\"\"\n",
    "#     # Get x, y, yerr from filename\n",
    "#     data = pd.read_csv(data_path, sep='\\s+')   \n",
    "#     x = data['qz'][2:]\n",
    "#     y = data['ref'][2:]\n",
    "#     yerr = data['error'][2:]\n",
    "#     # determine the output file name to save parameters\n",
    "#     param_file = output_path\n",
    "    \n",
    "#     # Loop for random trial of fitting (Monte Carlo part)\n",
    "#     chisq_min=1e6 # a very large number \n",
    "#     np.random.seed(1)\n",
    "#     record_collection=[]\n",
    "#     for count in range(loop_number):\n",
    "#         print(\"# {} fitting ...\".format(count))\n",
    "\n",
    "#         c=np.random.rand(len(param_lb))\n",
    "#         p0=param_lb+(param_ub-param_lb)*c\n",
    "#         t0=time.time()\n",
    "#         try:\n",
    "#             param_opt, param_cov=curve_fit(fitRef, x,\n",
    "#                                            y, p0, sigma=yerr, \n",
    "#                                            bounds=(param_lb,param_ub),\n",
    "#                                            maxfev = 1000)\n",
    "#             t1=time.time()\n",
    "#             print('Elapse time :', t1-t0)\n",
    "    \n",
    "#             chisq=getChisq(fitRef, param_opt, x, \n",
    "#                            y, yerr)\n",
    "#             if chisq<=chisq_min:\n",
    "#                 chisq_min=chisq\n",
    "#                 param_best=param_opt\n",
    "        \n",
    "#             print('chisq for this #{} run is : {}'.format(count ,chisq), \n",
    "#               end='  ')\n",
    "#             print(\"chisq best now is :\", chisq_min)\n",
    "#             print('-'*20)\n",
    "#             record={'attemp #': count, 'chisq':chisq, 'params':param_opt}\n",
    "#             record_collection.append(record)\n",
    "#         except RuntimeError as e:\n",
    "#             print(\"RuntimeError :\", e)\n",
    "#     print(\"Finally, best parameters is :------\\n\",param_best)\n",
    "     \n",
    "#     df=pd.DataFrame(record_collection)\n",
    "#     df.sort_values(by='chisq',inplace=True)\n",
    "#     df.reset_index(inplace=True)\n",
    "#     df.to_csv(param_file, index=False)\n",
    "#     print('Did save the params to :', param_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dbdca8a",
   "metadata": {},
   "source": [
    "## Step 4 :  Specify the conditions that need to run overnight\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e8b3f309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['R-water2-2e8300bc.txt',\n",
       " 'R-PEG2k_AgNP10_10mM_K-2910a595.txt',\n",
       " 'R-PEG5k_AgNP10_10mM_K-b765e504.txt',\n",
       " 'R-PEG20k_AgNP10_10mM_K-26bfde93.txt',\n",
       " 'R-PEG2k_AgNP10_0mM_PAA-d3b5980c.txt',\n",
       " 'R-PEG5k_AgNP10_100mM_K-f29435bd.txt',\n",
       " 'R-PEG2k_AgNP10_0.2mM_PAA-aca5b695.txt',\n",
       " 'R-PEG2k_AgNP10_2mM_PAA_HCl-36e34205.txt',\n",
       " 'R-PEG5k_AgNP10_0mM_PAA-6df89ea8.txt',\n",
       " 'R-PEG5k_AgNP10_0.2mM_PAA-38a03ec7.txt',\n",
       " 'R-PEG5k_AgNP10_2mM_PAA_HCl-3d4824d3.txt',\n",
       " 'R-PEG40k_AgNP10_0mM_PAA-10c56049.txt',\n",
       " 'R-PEG40k_AgNP10_0.2mM_PAA-5eff49f8.txt',\n",
       " 'R-PEG40k_AgNP10_2mM_PAA_HCl-10b2d544.txt',\n",
       " 'R-PEG5k_AuNP5_10mM_K-74115e32.txt',\n",
       " 'R-PEG2k_AgNP10_2mM_PAA-18f54ef1.txt',\n",
       " 'R-PEG20k_AgNP10_2mM_PAA_HCl-6adee353.txt']"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path=\"C:\\\\Users\\\\binay\\\\Box\\\\APS_Ag_Binary\\\\New_BNL_replacement\\\\data2\\\\\"\n",
    "# path_1=\"C:\\\\Users\\\\binay\\\\Box\\\\APS_Ag_Binary\\\\Analysis\\\\SPRRF\\\\\"\n",
    "name_tags = ['R'\n",
    "            # 'PEG2k',\n",
    "            #  'PEG5k',\n",
    "             # '10mM',\n",
    "             # '.txt',\n",
    "            ]\n",
    "\n",
    "file_names = glob.glob(path+'*'+'*'.join(name_tags)+'*')\n",
    "file_names.sort(key=os.path.getmtime)\n",
    "\n",
    "names = [os.path.basename(file) for file in file_names]\n",
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "348f03da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# os.listdir(os.path.join('Reflec data','wih HCl'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b4aeff0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'R_1': 'R-PEG40k_AgNP10_2mM_PAA_HCl-10b2d544.txt',\n",
       " 'R_2': 'R-PEG2k_AgNP10_2mM_PAA_HCl-36e34205.txt',\n",
       " 'R_4': 'R-PEG5k_AgNP10_2mM_PAA_HCl-3d4824d3.txt',\n",
       " 'R_6': 'R-PEG20k_AgNP10_2mM_PAA_HCl-6adee353.txt'}"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filenames = {\n",
    "    'R_1': 'R-PEG40k_AgNP10_2mM_PAA_HCl-10b2d544.txt',\n",
    "    'R_2': 'R-PEG2k_AgNP10_2mM_PAA_HCl-36e34205.txt',\n",
    "    'R_4': 'R-PEG5k_AgNP10_2mM_PAA_HCl-3d4824d3.txt',\n",
    "    'R_6': 'R-PEG20k_AgNP10_2mM_PAA_HCl-6adee353.txt',\n",
    "}\n",
    "\n",
    "filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d41e9515-f3a7-418d-8d6f-91b347ca9e95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = pd.read_csv(path+'T2-PEG2k-Ag10-1to1-PEG5k-Au5-K2CO3-1mM-scan598-601_ref.txt', sep='\\s+')\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "29ab090e-0123-439a-bca1-1179df9dd2f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "69b89313",
   "metadata": {},
   "outputs": [],
   "source": [
    "conds = ['R_1','R_2','R_4','R_6']\n",
    "path = path  # directory where data files are located\n",
    "output_dir = './results1'  # directory where output files will be saved\n",
    "\n",
    "# Create the output directory if it does not exist\n",
    "os.makedirs(output_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "7d0f9bac-61e5-4c55-886c-cea5a372d527",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# 0 fitting ...\n",
      "# 0 fitting ...\n",
      "# 0 fitting ...\n",
      "# 0 fitting ...\n",
      "Elapse time : 97.93355870246887\n",
      "chisq for this #0 run is : 0.3204506402996165  chisq best now is : 0.3204506402996165\n",
      "--------------------\n",
      "# 1 fitting ...\n",
      "Elapse time : 131.59294843673706\n",
      "chisq for this #0 run is : 0.3360456469641681  chisq best now is : 0.3360456469641681\n",
      "--------------------\n",
      "# 1 fitting ...\n",
      "Elapse time : 175.99915432929993\n",
      "chisq for this #0 run is : 0.30907428596435094  chisq best now is : 0.30907428596435094\n",
      "--------------------\n",
      "# 1 fitting ...\n",
      "Elapse time : 81.05260825157166\n",
      "chisq for this #1 run is : 0.32045064023899  chisq best now is : 0.32045064023899\n",
      "--------------------\n",
      "# 2 fitting ...\n",
      "Elapse time : 183.8332028388977\n",
      "chisq for this #0 run is : 0.4244338394855354  chisq best now is : 0.4244338394855354\n",
      "--------------------\n",
      "# 1 fitting ...\n",
      "Elapse time : 115.31722235679626\n",
      "chisq for this #2 run is : 0.3204506404660052  chisq best now is : 0.32045064023899\n",
      "--------------------\n",
      "# 3 fitting ...\n",
      "Elapse time : 173.80279636383057\n",
      "chisq for this #1 run is : 0.33604564876229137  chisq best now is : 0.3360456469641681\n",
      "--------------------\n",
      "# 2 fitting ...\n",
      "Elapse time : 122.74831509590149\n",
      "chisq for this #1 run is : 3.8410366857933127  chisq best now is : 0.4244338394855354\n",
      "--------------------\n",
      "# 2 fitting ...\n",
      "Elapse time : 172.6513330936432\n",
      "chisq for this #1 run is : 0.3090742859364641  chisq best now is : 0.3090742859364641\n",
      "--------------------\n",
      "# 2 fitting ...\n",
      "Elapse time : 135.22463083267212\n",
      "chisq for this #2 run is : 0.6530647741814813  chisq best now is : 0.4244338394855354\n",
      "--------------------\n",
      "# 3 fitting ...\n",
      "Elapse time : 181.2638647556305\n",
      "chisq for this #2 run is : 0.3390711361191744  chisq best now is : 0.3360456469641681\n",
      "--------------------\n",
      "# 3 fitting ...\n",
      "Elapse time : 185.78797316551208\n",
      "chisq for this #2 run is : 0.3090742857388216  chisq best now is : 0.3090742857388216\n",
      "--------------------\n",
      "# 3 fitting ...\n",
      "Elapse time : 123.56147289276123\n",
      "chisq for this #3 run is : 0.4244338389996709  chisq best now is : 0.4244338389996709\n",
      "--------------------\n",
      "# 4 fitting ...\n",
      "Elapse time : 315.8848235607147\n",
      "chisq for this #3 run is : 0.22358048804360495  chisq best now is : 0.22358048804360495\n",
      "--------------------\n",
      "# 4 fitting ...\n",
      "Elapse time : 117.9888231754303\n",
      "chisq for this #3 run is : 0.30907428581574353  chisq best now is : 0.3090742857388216\n",
      "--------------------\n",
      "# 4 fitting ...\n",
      "Elapse time : 122.074711561203\n",
      "chisq for this #4 run is : 0.4551188955734116  chisq best now is : 0.22358048804360495\n",
      "--------------------\n",
      "# 5 fitting ...\n",
      "Elapse time : 84.92901945114136\n",
      "chisq for this #4 run is : 1.8337771773219969  chisq best now is : 0.3090742857388216\n",
      "--------------------\n",
      "# 5 fitting ...\n",
      "Elapse time : 199.51562452316284\n",
      "chisq for this #4 run is : 0.3020070336258433  chisq best now is : 0.3020070336258433\n",
      "--------------------\n",
      "# 5 fitting ...\n",
      "Elapse time : 319.6942102909088\n",
      "chisq for this #3 run is : 0.3334713465473478  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 4 fitting ...\n",
      "Elapse time : 69.52114391326904\n",
      "chisq for this #5 run is : 2.0347440424344674  chisq best now is : 0.3020070336258433\n",
      "--------------------\n",
      "# 6 fitting ...\n",
      "Elapse time : 112.69171500205994\n",
      "chisq for this #5 run is : 0.45513097903706856  chisq best now is : 0.22358048804360495\n",
      "--------------------\n",
      "# 6 fitting ...\n",
      "Elapse time : 125.57873892784119\n",
      "chisq for this #5 run is : 0.3090742858966451  chisq best now is : 0.3090742857388216\n",
      "--------------------\n",
      "# 6 fitting ...\n",
      "Elapse time : 170.4855077266693\n",
      "chisq for this #4 run is : 0.33604565764507277  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 5 fitting ...\n",
      "Elapse time : 143.22145223617554\n",
      "chisq for this #6 run is : 2.034744042180255  chisq best now is : 0.3020070336258433\n",
      "--------------------\n",
      "# 7 fitting ...\n",
      "Elapse time : 117.6282947063446\n",
      "chisq for this #6 run is : 0.3090742857370893  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 7 fitting ...\n",
      "Elapse time : 250.5955455303192\n",
      "chisq for this #6 run is : 0.446959989781854  chisq best now is : 0.22358048804360495\n",
      "--------------------\n",
      "# 7 fitting ...\n",
      "Elapse time : 135.58266067504883\n",
      "chisq for this #7 run is : 0.30907428577117396  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 8 fitting ...\n",
      "Elapse time : 140.5922873020172\n",
      "chisq for this #7 run is : 0.42443384223757435  chisq best now is : 0.3020070336258433\n",
      "--------------------\n",
      "# 8 fitting ...\n",
      "Elapse time : 166.80315566062927\n",
      "chisq for this #5 run is : 0.3390669093172848  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 6 fitting ...\n",
      "Elapse time : 120.0673770904541\n",
      "chisq for this #7 run is : 0.516168139690073  chisq best now is : 0.22358048804360495\n",
      "--------------------\n",
      "# 8 fitting ...\n",
      "Elapse time : 140.71760272979736\n",
      "chisq for this #6 run is : 0.3360456481573191  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 7 fitting ...\n",
      "Elapse time : 168.27490162849426\n",
      "chisq for this #8 run is : 0.30200702952412245  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 9 fitting ...\n",
      "Elapse time : 248.18671011924744\n",
      "chisq for this #8 run is : 0.3356375007304514  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 9 fitting ...\n",
      "Elapse time : 168.6259548664093\n",
      "chisq for this #8 run is : 0.8444172855021755  chisq best now is : 0.22358048804360495\n",
      "--------------------\n",
      "# 9 fitting ...\n",
      "Elapse time : 121.5134527683258\n",
      "chisq for this #7 run is : 0.33604567294316556  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 8 fitting ...\n",
      "Elapse time : 159.6355106830597\n",
      "chisq for this #9 run is : 0.30200703757395186  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 10 fitting ...\n",
      "Elapse time : 111.63976740837097\n",
      "chisq for this #9 run is : 0.3204506401288459  chisq best now is : 0.22358048804360495\n",
      "--------------------\n",
      "# 10 fitting ...\n",
      "Elapse time : 128.1734390258789\n",
      "chisq for this #8 run is : 0.625571774429881  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 9 fitting ...\n",
      "Elapse time : 177.31295084953308\n",
      "chisq for this #9 run is : 0.3378168019700534  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 10 fitting ...\n",
      "Elapse time : 130.54154706001282\n",
      "chisq for this #10 run is : 0.4321483918088393  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 11 fitting ...\n",
      "Elapse time : 107.38492512702942\n",
      "chisq for this #10 run is : 0.32045064014990254  chisq best now is : 0.22358048804360495\n",
      "--------------------\n",
      "# 11 fitting ...\n",
      "Elapse time : 101.84220337867737\n",
      "chisq for this #10 run is : 0.3378168022089637  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 11 fitting ...\n",
      "Elapse time : 86.43106174468994\n",
      "chisq for this #11 run is : 1.3061496208138998  chisq best now is : 0.22358048804360495\n",
      "--------------------\n",
      "# 12 fitting ...\n",
      "Elapse time : 119.93933606147766\n",
      "chisq for this #11 run is : 0.4244338420227358  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 12 fitting ...\n",
      "Elapse time : 70.09357333183289\n",
      "chisq for this #12 run is : 0.4551245849322295  chisq best now is : 0.22358048804360495\n",
      "--------------------\n",
      "# 13 fitting ...\n",
      "Elapse time : 121.5398588180542\n",
      "chisq for this #11 run is : 1.2370820596886598  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 12 fitting ...\n",
      "Elapse time : 156.67441296577454\n",
      "chisq for this #12 run is : 0.43214839199154903  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 13 fitting ...\n",
      "Elapse time : 121.85417222976685\n",
      "chisq for this #13 run is : 0.32045064050983235  chisq best now is : 0.22358048804360495\n",
      "--------------------\n",
      "# 14 fitting ...\n",
      "Elapse time : 68.33688402175903\n",
      "chisq for this #13 run is : 0.79511781847928  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 14 fitting ...\n",
      "Elapse time : 390.85529017448425\n",
      "chisq for this #9 run is : 0.3390510057813959  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 10 fitting ...\n",
      "Elapse time : 95.71070718765259\n",
      "chisq for this #14 run is : 0.4381742030822231  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 15 fitting ...\n",
      "Elapse time : 305.1387686729431\n",
      "chisq for this #12 run is : 0.3356375032001025  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 13 fitting ...\n",
      "Elapse time : 175.52233147621155\n",
      "chisq for this #10 run is : 0.3360456519855226  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 11 fitting ...\n",
      "Elapse time : 87.11509895324707\n",
      "chisq for this #15 run is : 0.30200705152639773  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 16 fitting ...\n",
      "Elapse time : 71.12468671798706\n",
      "chisq for this #16 run is : 0.43214839174923914  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 17 fitting ...\n",
      "Elapse time : 134.0436019897461\n",
      "chisq for this #13 run is : 0.33781680129858593  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 14 fitting ...\n",
      "Elapse time : 123.07597088813782\n",
      "chisq for this #11 run is : 0.8048743436877924  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 12 fitting ...\n",
      "Elapse time : 384.36465764045715\n",
      "chisq for this #14 run is : 0.22358048736957978  chisq best now is : 0.22358048736957978\n",
      "--------------------\n",
      "# 15 fitting ...\n",
      "Elapse time : 111.85674524307251\n",
      "chisq for this #17 run is : 0.4244338419131217  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 18 fitting ...\n",
      "Elapse time : 123.99343943595886\n",
      "chisq for this #14 run is : 0.30907428578474044  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 15 fitting ...\n",
      "Elapse time : 114.50454235076904\n",
      "chisq for this #12 run is : 0.7688980555126664  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 13 fitting ...\n",
      "Elapse time : 76.12149333953857\n",
      "chisq for this #15 run is : 0.3204506402989363  chisq best now is : 0.22358048736957978\n",
      "--------------------\n",
      "# 16 fitting ...\n",
      "Elapse time : 113.11887764930725\n",
      "chisq for this #15 run is : 0.33781680120769286  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 16 fitting ...\n",
      "Elapse time : 103.16281580924988\n",
      "chisq for this #16 run is : 0.32045064019913  chisq best now is : 0.22358048736957978\n",
      "--------------------\n",
      "# 17 fitting ...\n",
      "Elapse time : 228.41767716407776\n",
      "chisq for this #18 run is : 0.30200703134437973  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 19 fitting ...\n",
      "Elapse time : 65.05535650253296\n",
      "chisq for this #19 run is : 1.126164253226096  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 20 fitting ...\n",
      "Elapse time : 167.48664140701294\n",
      "chisq for this #16 run is : 0.3090742862118866  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 17 fitting ...\n",
      "Elapse time : 352.2801878452301\n",
      "chisq for this #13 run is : 0.33898075780496156  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 14 fitting ...\n",
      "Elapse time : 183.44065642356873\n",
      "chisq for this #20 run is : 0.5618284590576358  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 21 fitting ...\n",
      "Elapse time : 116.1140615940094\n",
      "chisq for this #14 run is : 0.33604564616693905  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 15 fitting ...\n",
      "Elapse time : 251.57821655273438\n",
      "chisq for this #17 run is : 0.3356374966208922  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 18 fitting ...\n",
      "Elapse time : 462.6181676387787\n",
      "chisq for this #17 run is : 0.22358048787369766  chisq best now is : 0.22358048736957978\n",
      "--------------------\n",
      "# 18 fitting ...\n",
      "Elapse time : 176.71141242980957\n",
      "chisq for this #21 run is : 0.30200703715976757  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 22 fitting ...\n",
      "Elapse time : 167.8743143081665\n",
      "chisq for this #15 run is : 0.3390667629023014  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 16 fitting ...\n",
      "Elapse time : 115.5507960319519\n",
      "chisq for this #18 run is : 0.33781680637193046  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 19 fitting ...\n",
      "Elapse time : 157.18388748168945\n",
      "chisq for this #18 run is : 0.3204506401638887  chisq best now is : 0.22358048736957978\n",
      "--------------------\n",
      "# 19 fitting ...\n",
      "Elapse time : 154.76618790626526\n",
      "chisq for this #19 run is : 0.30907428575023094  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 20 fitting ...\n",
      "Elapse time : 201.94507312774658\n",
      "chisq for this #22 run is : 0.30200703098636  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 23 fitting ...\n",
      "Elapse time : 205.24387311935425\n",
      "chisq for this #16 run is : 0.33604565657919844  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 17 fitting ...\n",
      "Elapse time : 59.5693564414978\n",
      "chisq for this #23 run is : 2.5443491296482272  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 24 fitting ...\n",
      "Elapse time : 106.85090327262878\n",
      "chisq for this #20 run is : 1.6939733778365103  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 21 fitting ...\n",
      "Elapse time : 60.69587540626526\n",
      "chisq for this #17 run is : 0.3372660412661851  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 18 fitting ...\n",
      "Elapse time : 77.47905135154724\n",
      "chisq for this #21 run is : 1.8337771771821365  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 22 fitting ...\n",
      "Elapse time : 170.80871510505676\n",
      "chisq for this #24 run is : 0.302007034414044  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 25 fitting ...\n",
      "Elapse time : 144.06812500953674\n",
      "chisq for this #18 run is : 0.33604564682796884  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 19 fitting ...\n",
      "Elapse time : 395.6131429672241\n",
      "chisq for this #19 run is : 0.22358048751771833  chisq best now is : 0.22358048736957978\n",
      "--------------------\n",
      "# 20 fitting ...\n",
      "Elapse time : 196.68947410583496\n",
      "chisq for this #22 run is : 0.3378168015356742  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 23 fitting ...\n",
      "Elapse time : 156.4072573184967\n",
      "chisq for this #25 run is : 0.4244338401558596  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 26 fitting ...\n",
      "Elapse time : 148.97396898269653\n",
      "chisq for this #19 run is : 0.3390690117332154  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 20 fitting ...\n",
      "Elapse time : 104.16399264335632\n",
      "chisq for this #20 run is : 0.32045064028549447  chisq best now is : 0.22358048736957978\n",
      "--------------------\n",
      "# 21 fitting ...\n",
      "Elapse time : 121.80637979507446\n",
      "chisq for this #26 run is : 0.56182845898327  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 27 fitting ...\n",
      "Elapse time : 160.1506404876709\n",
      "chisq for this #23 run is : 0.30907428574082785  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 24 fitting ...\n",
      "Elapse time : 161.6656370162964\n",
      "chisq for this #20 run is : 0.33604567459965995  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 21 fitting ...\n",
      "Elapse time : 56.400989055633545\n",
      "chisq for this #27 run is : 1.1121281690769804  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 28 fitting ...\n",
      "Elapse time : 79.3358678817749\n",
      "chisq for this #21 run is : 0.6374691850814206  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 22 fitting ...\n",
      "Elapse time : 146.87387895584106\n",
      "chisq for this #28 run is : 0.4244338390952306  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 29 fitting ...\n",
      "Elapse time : 201.68663096427917\n",
      "chisq for this #24 run is : 0.30907428577028107  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 25 fitting ...\n",
      "Elapse time : 161.62461757659912\n",
      "chisq for this #22 run is : 0.3360456535450334  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 23 fitting ...\n",
      "Elapse time : 128.456933259964\n",
      "chisq for this #25 run is : 0.30907428576564067  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 26 fitting ...\n",
      "Elapse time : 442.2603175640106\n",
      "chisq for this #21 run is : 0.22358048735651032  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 22 fitting ...\n",
      "Elapse time : 157.76552271842957\n",
      "chisq for this #29 run is : 0.44073038158525735  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 30 fitting ...\n",
      "Elapse time : 149.08836674690247\n",
      "chisq for this #23 run is : 0.3360456718931327  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 24 fitting ...\n",
      "Elapse time : 84.88348531723022\n",
      "chisq for this #22 run is : 0.45511490493328294  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 23 fitting ...\n",
      "Elapse time : 111.45245289802551\n",
      "chisq for this #26 run is : 1.359335399370972  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 27 fitting ...\n",
      "Elapse time : 127.82071280479431\n",
      "chisq for this #30 run is : 0.43817420305592464  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 31 fitting ...\n",
      "Elapse time : 125.85561609268188\n",
      "chisq for this #23 run is : 0.32045064017660363  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 24 fitting ...\n",
      "Elapse time : 202.004821062088\n",
      "chisq for this #24 run is : 0.3390702226570313  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 25 fitting ...\n",
      "Elapse time : 177.97860097885132\n",
      "chisq for this #31 run is : 0.30200704140243345  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 32 fitting ...\n",
      "Elapse time : 220.19420289993286\n",
      "chisq for this #27 run is : 0.33781680135030373  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 28 fitting ...\n",
      "Elapse time : 135.1743700504303\n",
      "chisq for this #24 run is : 0.32045064014718794  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 25 fitting ...\n",
      "Elapse time : 190.400616645813\n",
      "chisq for this #25 run is : 0.3360456572281128  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 26 fitting ...\n",
      "Elapse time : 178.14370107650757\n",
      "chisq for this #32 run is : 0.4244338401225784  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 33 fitting ...\n",
      "Elapse time : 151.35039234161377\n",
      "chisq for this #26 run is : 0.33907069893787145  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 27 fitting ...\n",
      "Elapse time : 329.65289545059204\n",
      "chisq for this #28 run is : 0.335637504680368  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 29 fitting ...\n",
      "Elapse time : 315.97045946121216\n",
      "chisq for this #25 run is : 0.22367596037771717  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 26 fitting ...\n",
      "Elapse time : 177.832293510437\n",
      "chisq for this #33 run is : 0.5618284589155559  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 34 fitting ...\n",
      "Elapse time : 113.37869429588318\n",
      "chisq for this #27 run is : 0.33604565651199897  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 28 fitting ...\n",
      "Elapse time : 102.3925051689148\n",
      "chisq for this #29 run is : 2.047713203551185  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 30 fitting ...\n",
      "Elapse time : 236.70381212234497\n",
      "chisq for this #34 run is : 0.7486100531399011  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 35 fitting ...\n",
      "Elapse time : 181.5083520412445\n",
      "chisq for this #28 run is : 0.3390708682293866  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 29 fitting ...\n",
      "Elapse time : 176.53666591644287\n",
      "chisq for this #30 run is : 0.3090742858708502  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 31 fitting ...\n",
      "Elapse time : 315.17820501327515\n",
      "chisq for this #26 run is : 0.4469599915539963  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 27 fitting ...\n",
      "Elapse time : 79.53060340881348\n",
      "chisq for this #35 run is : 0.4381742031579904  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 36 fitting ...\n",
      "Elapse time : 142.36313462257385\n",
      "Elapse time : 102.35509943962097\n",
      "chisq for this #31 run is : 0.337816800909274  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 32 fitting ...\n",
      "chisq for this #27 run is : 0.32045064015811126  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 28 fitting ...\n",
      "Elapse time : 176.0344169139862\n",
      "chisq for this #29 run is : 0.33604565337578324  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 30 fitting ...\n",
      "Elapse time : 153.16250205039978\n",
      "chisq for this #36 run is : 0.3020070458308633  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 37 fitting ...\n",
      "Elapse time : 143.0485758781433\n",
      "chisq for this #28 run is : 0.3204506401825287  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 29 fitting ...\n",
      "Elapse time : 136.06655859947205\n",
      "chisq for this #30 run is : 0.3360456501692548  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 31 fitting ...\n",
      "Elapse time : 96.84519934654236\n",
      "chisq for this #37 run is : 0.4381742029881658  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 38 fitting ...\n",
      "Elapse time : 163.0194730758667\n",
      "chisq for this #32 run is : 0.30907428594982156  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 33 fitting ...\n",
      "Elapse time : 65.5082266330719\n",
      "chisq for this #29 run is : 1.4943361616070627  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 30 fitting ...\n",
      "Elapse time : 168.1923384666443\n",
      "chisq for this #31 run is : 0.33604564735838294  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 32 fitting ...\n",
      "Elapse time : 214.56123065948486\n",
      "chisq for this #38 run is : 0.7486100546537546  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 39 fitting ...\n",
      "Elapse time : 246.23432445526123\n",
      "chisq for this #33 run is : 0.3356375242706948  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 34 fitting ...\n",
      "Elapse time : 178.2135510444641\n",
      "chisq for this #39 run is : 0.4244338390059051  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 40 fitting ...\n",
      "Elapse time : 190.2811918258667\n",
      "chisq for this #34 run is : 0.33781680090779626  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 35 fitting ...\n",
      "Elapse time : 433.18123722076416\n",
      "chisq for this #30 run is : 0.22358048738538508  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 31 fitting ...\n",
      "Elapse time : 336.286358833313\n",
      "chisq for this #32 run is : 0.3390661904531621  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 33 fitting ...\n",
      "Elapse time : 170.2728509902954\n",
      "chisq for this #40 run is : 0.30200703047853245  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 41 fitting ...\n",
      "Elapse time : 96.12446880340576\n",
      "chisq for this #31 run is : 0.4551148913172211  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 32 fitting ...\n",
      "Elapse time : 122.72632098197937\n",
      "chisq for this #32 run is : 0.3204506403437258  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 33 fitting ...\n",
      "Elapse time : 281.2883732318878\n",
      "chisq for this #35 run is : 0.33563749534190224  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 36 fitting ...\n",
      "Elapse time : 175.86878943443298\n",
      "chisq for this #41 run is : 0.42443384083510527  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 42 fitting ...\n",
      "Elapse time : 266.8752610683441\n",
      "chisq for this #33 run is : 0.33906881798324545  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 34 fitting ...\n",
      "Elapse time : 74.82931804656982\n",
      "chisq for this #33 run is : 0.45510836769567903  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 34 fitting ...\n",
      "Elapse time : 114.52828669548035\n",
      "chisq for this #36 run is : 1.2370820628669073  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 37 fitting ...\n",
      "Elapse time : 97.9707190990448\n",
      "chisq for this #34 run is : 0.4551149771449765  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 35 fitting ...\n",
      "Elapse time : 136.48109316825867\n",
      "chisq for this #34 run is : 0.3360456498135393  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 35 fitting ...\n",
      "Elapse time : 188.78447341918945\n",
      "chisq for this #42 run is : 0.42443383883044605  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 43 fitting ...\n",
      "Elapse time : 126.2318503856659\n",
      "chisq for this #37 run is : 0.309074285840931  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 38 fitting ...\n",
      "Elapse time : 108.48556518554688\n",
      "chisq for this #35 run is : 0.33604565766667804  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 36 fitting ...\n",
      "Elapse time : 100.94822335243225\n",
      "chisq for this #43 run is : 0.4244338431503943  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 44 fitting ...\n",
      "Elapse time : 106.7292001247406\n",
      "chisq for this #36 run is : 0.3372660400198822  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 37 fitting ...\n",
      "Elapse time : 188.73566007614136\n",
      "chisq for this #44 run is : 0.3020070311091075  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 45 fitting ...\n",
      "Elapse time : 363.7979807853699\n",
      "chisq for this #35 run is : 0.22358048750285972  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 36 fitting ...\n",
      "Elapse time : 297.02747631073\n",
      "chisq for this #38 run is : 0.33563748799720533  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 39 fitting ...\n",
      "Elapse time : 165.46075820922852\n",
      "chisq for this #37 run is : 0.33604566800462676  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 38 fitting ...\n",
      "Elapse time : 144.7771282196045\n",
      "chisq for this #36 run is : 0.455125150272333  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 37 fitting ...\n",
      "Elapse time : 200.67018127441406\n",
      "chisq for this #45 run is : 0.30200703373158644  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 46 fitting ...\n",
      "Elapse time : 165.79249906539917\n",
      "chisq for this #39 run is : 0.3090742858491769  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 40 fitting ...\n",
      "Elapse time : 189.50488257408142\n",
      "chisq for this #38 run is : 0.33906782186865386  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 39 fitting ...\n",
      "Elapse time : 124.67814040184021\n",
      "chisq for this #46 run is : 0.4321483917210462  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 47 fitting ...\n",
      "Elapse time : 170.19174003601074\n",
      "chisq for this #40 run is : 0.3090742857817021  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 41 fitting ...\n",
      "Elapse time : 147.96861934661865\n",
      "chisq for this #39 run is : 0.3390696262540578  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 40 fitting ...\n",
      "Elapse time : 91.56298041343689\n",
      "chisq for this #41 run is : 1.8337771771767841  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 42 fitting ...\n",
      "Elapse time : 203.59561467170715\n",
      "chisq for this #47 run is : 0.6530989504649279  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 48 fitting ...\n",
      "Elapse time : 85.51722955703735\n",
      "chisq for this #42 run is : 1.8337771771739486  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 43 fitting ...\n",
      "Elapse time : 159.1838493347168\n",
      "chisq for this #40 run is : 0.33604564663489195  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 41 fitting ...\n",
      "Elapse time : 503.77190017700195\n",
      "chisq for this #37 run is : 0.22358048736360686  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 38 fitting ...\n",
      "Elapse time : 137.8197476863861\n",
      "chisq for this #41 run is : 0.3360456459622227  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 42 fitting ...\n",
      "Elapse time : 191.55899596214294\n",
      "chisq for this #48 run is : 0.42443383900975434  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 49 fitting ...\n",
      "Elapse time : 79.32070469856262\n",
      "chisq for this #38 run is : 0.32045064012504854  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 39 fitting ...\n",
      "Elapse time : 64.96917009353638\n",
      "chisq for this #49 run is : 0.4381742029208985  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 50 fitting ...\n",
      "Elapse time : 231.26074051856995\n",
      "chisq for this #43 run is : 0.33563752933907476  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 44 fitting ...\n",
      "Elapse time : 96.6859519481659\n",
      "chisq for this #44 run is : 0.3090742857799623  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 45 fitting ...\n",
      "Elapse time : 192.69526410102844\n",
      "chisq for this #50 run is : 3.730562947483386  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 51 fitting ...\n",
      "Elapse time : 121.11434245109558\n",
      "chisq for this #45 run is : 0.3090742857669142  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 46 fitting ...\n",
      "Elapse time : 310.85667991638184\n",
      "chisq for this #39 run is : 0.22358048739103883  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 40 fitting ...\n",
      "Elapse time : 427.36094427108765\n",
      "chisq for this #42 run is : 0.5475383127080065  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 43 fitting ...\n",
      "Elapse time : 133.02644205093384\n",
      "chisq for this #46 run is : 1.6577092497827925  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 47 fitting ...\n",
      "Elapse time : 175.11202788352966\n",
      "chisq for this #51 run is : 0.3020070334090757  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 52 fitting ...\n",
      "Elapse time : 139.48053312301636\n",
      "chisq for this #40 run is : 0.3204506401476169  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 41 fitting ...\n",
      "Elapse time : 132.7466859817505\n",
      "chisq for this #52 run is : 0.302007033841462  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 53 fitting ...\n",
      "Elapse time : 156.08220076560974\n",
      "chisq for this #43 run is : 0.33604564778066365  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 44 fitting ...\n",
      "Elapse time : 161.91488122940063\n",
      "Elapse time : 100.95120859146118\n",
      "chisq for this #47 run is : 0.3378168010456643  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 48 fitting ...\n",
      "chisq for this #41 run is : 0.32045064052155364  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 42 fitting ...\n",
      "Elapse time : 129.86083221435547\n",
      "chisq for this #44 run is : 0.5477916467828524  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 45 fitting ...\n",
      "Elapse time : 132.85879611968994\n",
      "chisq for this #42 run is : 0.3204506401759176  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 43 fitting ...\n",
      "Elapse time : 135.58421277999878\n",
      "chisq for this #48 run is : 0.3378168016022305  chisq best now is : 0.3090742857370893\n",
      "--------------------\n",
      "# 49 fitting ...\n",
      "Elapse time : 199.67876434326172\n",
      "chisq for this #53 run is : 0.30200703599108825  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 54 fitting ...\n",
      "Elapse time : 86.57786893844604\n",
      "chisq for this #43 run is : 0.3204506405966463  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 44 fitting ...\n",
      "Elapse time : 173.89712500572205\n",
      "chisq for this #49 run is : 0.3090742857262205  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 50 fitting ...\n",
      "Elapse time : 122.47174715995789\n",
      "chisq for this #44 run is : 0.32045064021654934  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 45 fitting ...\n",
      "Elapse time : 177.1826467514038\n",
      "chisq for this #54 run is : 0.42443384537362966  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 55 fitting ...\n",
      "Elapse time : 112.60249638557434\n",
      "chisq for this #45 run is : 0.320450640386152  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 46 fitting ...\n",
      "Elapse time : 115.77573704719543\n",
      "chisq for this #55 run is : 0.43817420297558674  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 56 fitting ...\n",
      "Elapse time : 168.77199459075928\n",
      "chisq for this #50 run is : 0.30907428574638024  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 51 fitting ...\n",
      "Elapse time : 397.74814462661743\n",
      "chisq for this #45 run is : 0.3389807658197041  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 46 fitting ...\n",
      "Elapse time : 65.65854048728943\n",
      "chisq for this #46 run is : 0.4551256791653809  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 47 fitting ...\n",
      "Elapse time : 155.1667082309723\n",
      "chisq for this #56 run is : 2.034744042522109  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 57 fitting ...\n",
      "Elapse time : 162.02481603622437\n",
      "chisq for this #51 run is : 0.30907428582017915  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 52 fitting ...\n",
      "Elapse time : 132.02683877944946\n",
      "chisq for this #46 run is : 0.7677546489057242  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 47 fitting ...\n",
      "Elapse time : 204.48382663726807\n",
      "chisq for this #47 run is : 0.44695999243250756  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 48 fitting ...\n",
      "Elapse time : 109.14223194122314\n",
      "chisq for this #57 run is : 3.734026714152268  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 58 fitting ...\n",
      "Elapse time : 101.4447910785675\n",
      "chisq for this #52 run is : 0.3090742863220868  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 53 fitting ...\n",
      "Elapse time : 152.81496000289917\n",
      "chisq for this #47 run is : 0.3390700613860695  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 48 fitting ...\n",
      "Elapse time : 71.96267127990723\n",
      "chisq for this #53 run is : 1.8337771772511453  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 54 fitting ...\n",
      "Elapse time : 100.06415224075317\n",
      "chisq for this #48 run is : 0.320450640209842  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 49 fitting ...\n",
      "Elapse time : 117.60664987564087\n",
      "chisq for this #58 run is : 0.6098215926186229  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 59 fitting ...\n",
      "Elapse time : 47.43285870552063\n",
      "chisq for this #59 run is : 3.7275719020650935  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 60 fitting ...\n",
      "Elapse time : 121.68897891044617\n",
      "chisq for this #48 run is : 0.33604565010029025  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 49 fitting ...\n",
      "Elapse time : 156.72970986366272\n",
      "chisq for this #54 run is : 0.309074285764321  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 55 fitting ...\n",
      "Elapse time : 130.78500199317932\n",
      "chisq for this #49 run is : 0.33604565202362713  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 50 fitting ...\n",
      "Elapse time : 172.92652201652527\n",
      "chisq for this #60 run is : 0.4244338410832897  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 61 fitting ...\n",
      "Elapse time : 167.3321032524109\n",
      "chisq for this #55 run is : 0.3090742857544003  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 56 fitting ...\n",
      "Elapse time : 359.6419198513031\n",
      "chisq for this #49 run is : 0.2235804881516464  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 50 fitting ...\n",
      "Elapse time : 187.9017369747162\n",
      "chisq for this #50 run is : 0.6199457913840547  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 51 fitting ...\n",
      "Elapse time : 167.24414610862732\n",
      "chisq for this #61 run is : 0.30200703332226453  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 62 fitting ...\n",
      "Elapse time : 109.81263160705566\n",
      "chisq for this #56 run is : 0.3378168012630531  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 57 fitting ...\n",
      "Elapse time : 100.67441415786743\n",
      "chisq for this #51 run is : 0.3372660398728562  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 52 fitting ...\n",
      "Elapse time : 107.56494569778442\n",
      "chisq for this #62 run is : 0.5618284592696786  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 63 fitting ...\n",
      "Elapse time : 149.8144450187683\n",
      "chisq for this #57 run is : 1.8337771771750677  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 58 fitting ...\n",
      "Elapse time : 103.81113696098328\n",
      "chisq for this #52 run is : 0.3360456453695632  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 53 fitting ...\n",
      "Elapse time : 126.86096405982971\n",
      "chisq for this #58 run is : 1.237082062441214  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 59 fitting ...\n",
      "Elapse time : 241.725821018219\n",
      "chisq for this #63 run is : 0.4321483931340833  chisq best now is : 0.30200702952412245\n",
      "--------------------\n",
      "# 64 fitting ...\n",
      "Elapse time : 181.15269827842712\n",
      "chisq for this #53 run is : 0.3390714907301312  chisq best now is : 0.3334713465473478\n",
      "--------------------\n",
      "# 54 fitting ...\n",
      "Elapse time : 126.09797596931458\n",
      "chisq for this #59 run is : 0.3090742857454402  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 60 fitting ...\n",
      "Elapse time : 531.6671178340912\n",
      "chisq for this #50 run is : 0.2235804874969406  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 51 fitting ...\n",
      "Elapse time : 200.84920454025269\n",
      "chisq for this #64 run is : 0.30200702934260026  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 65 fitting ...\n",
      "Elapse time : 176.14070963859558\n",
      "chisq for this #60 run is : 0.3090742858203336  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 61 fitting ...\n",
      "Elapse time : 215.79064440727234\n",
      "chisq for this #54 run is : 0.3334713463309767  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 55 fitting ...\n",
      "Elapse time : 136.53799486160278\n",
      "chisq for this #51 run is : 0.3204506402579458  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 52 fitting ...\n",
      "Elapse time : 136.11049342155457\n",
      "chisq for this #65 run is : 0.4411421624748099  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 66 fitting ...\n",
      "Elapse time : 106.71122360229492\n",
      "chisq for this #61 run is : 0.30907428593037933  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 62 fitting ...\n",
      "Elapse time : 115.36010098457336\n",
      "chisq for this #55 run is : 0.33906525397224746  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 56 fitting ...\n",
      "Elapse time : 183.696519613266\n",
      "chisq for this #52 run is : 0.3204506403094748  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 53 fitting ...\n",
      "Elapse time : 102.53619408607483\n",
      "chisq for this #56 run is : 0.3390656357277712  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 57 fitting ...\n",
      "Elapse time : 85.47897791862488\n",
      "chisq for this #53 run is : 0.4551148911762261  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 54 fitting ...\n",
      "Elapse time : 193.98415613174438\n",
      "chisq for this #66 run is : 0.3020070375548165  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 67 fitting ...\n",
      "Elapse time : 207.8688609600067\n",
      "chisq for this #62 run is : 0.3356375575939333  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 63 fitting ...\n",
      "Elapse time : 139.0439841747284\n",
      "chisq for this #57 run is : 0.3360456482650037  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 58 fitting ...\n",
      "Elapse time : 88.82256269454956\n",
      "chisq for this #54 run is : 0.45511485413948827  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 55 fitting ...\n",
      "Elapse time : 107.7810971736908\n",
      "chisq for this #67 run is : 0.6532475930119419  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 68 fitting ...\n",
      "Elapse time : 123.48911595344543\n",
      "chisq for this #63 run is : 0.33781680403029846  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 64 fitting ...\n",
      "Elapse time : 134.6166114807129\n",
      "chisq for this #58 run is : 0.33906705096645673  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 59 fitting ...\n",
      "Elapse time : 172.85893034934998\n",
      "chisq for this #68 run is : 0.30200703489211006  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 69 fitting ...\n",
      "Elapse time : 145.35348916053772\n",
      "chisq for this #64 run is : 0.30907428592891806  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 65 fitting ...\n",
      "Elapse time : 82.6784098148346\n",
      "chisq for this #59 run is : 0.33604565150596855  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 60 fitting ...\n",
      "Elapse time : 108.82382321357727\n",
      "chisq for this #60 run is : 0.33604565191750435  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 61 fitting ...\n",
      "Elapse time : 144.49938416481018\n",
      "chisq for this #65 run is : 0.3378168062728807  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 66 fitting ...\n",
      "Elapse time : 171.90706777572632\n",
      "chisq for this #69 run is : 0.609821592636393  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 70 fitting ...\n",
      "Elapse time : 105.92632555961609\n",
      "chisq for this #61 run is : 0.3360456619164024  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 62 fitting ...\n",
      "Elapse time : 423.802531003952\n",
      "chisq for this #55 run is : 0.22512452687092036  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 56 fitting ...\n",
      "Elapse time : 172.93140196800232\n",
      "chisq for this #70 run is : 0.42443384177176313  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 71 fitting ...\n",
      "Elapse time : 132.7097146511078\n",
      "chisq for this #62 run is : 0.3360456716526369  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 63 fitting ...\n",
      "Elapse time : 140.25211358070374\n",
      "chisq for this #56 run is : 0.3204506402152137  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 57 fitting ...\n",
      "Elapse time : 254.3094162940979\n",
      "chisq for this #66 run is : 0.3356375750316822  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 67 fitting ...\n",
      "Elapse time : 73.84945178031921\n",
      "chisq for this #71 run is : 0.8667157963498541  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 72 fitting ...\n",
      "Elapse time : 145.02955746650696\n",
      "chisq for this #63 run is : 0.33604567462528206  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 64 fitting ...\n",
      "Elapse time : 142.30592155456543\n",
      "chisq for this #67 run is : 0.30907428597913417  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 68 fitting ...\n",
      "Elapse time : 192.40042400360107\n",
      "chisq for this #72 run is : 0.3020070294453705  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 73 fitting ...\n",
      "Elapse time : 118.66412854194641\n",
      "chisq for this #64 run is : 0.34685256886865884  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 65 fitting ...\n",
      "Elapse time : 166.45379734039307\n",
      "chisq for this #68 run is : 0.30907428573544343  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 69 fitting ...\n",
      "Elapse time : 183.15709710121155\n",
      "chisq for this #73 run is : 0.3020070434187022  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 74 fitting ...\n",
      "Elapse time : 196.4993860721588\n",
      "chisq for this #65 run is : 0.6199457947354404  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 66 fitting ...\n",
      "Elapse time : 459.28094458580017\n",
      "chisq for this #57 run is : 0.2235804879544241  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 58 fitting ...\n",
      "Elapse time : 120.25453758239746\n",
      "chisq for this #69 run is : 0.30907428644914314  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 70 fitting ...\n",
      "Elapse time : 164.0445840358734\n",
      "chisq for this #70 run is : 1.2370820599614842  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 71 fitting ...\n",
      "Elapse time : 206.62169432640076\n",
      "chisq for this #74 run is : 0.30200703239997984  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 75 fitting ...\n",
      "Elapse time : 198.73880171775818\n",
      "chisq for this #66 run is : 0.3390704382753728  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 67 fitting ...\n",
      "Elapse time : 145.78125286102295\n",
      "chisq for this #71 run is : 0.3090742857376035  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 72 fitting ...\n",
      "Elapse time : 179.69109296798706\n",
      "chisq for this #75 run is : 0.4244338393246866  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 76 fitting ...\n",
      "Elapse time : 188.79546070098877\n",
      "chisq for this #67 run is : 0.3390681539831395  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 68 fitting ...\n",
      "Elapse time : 389.4067368507385\n",
      "chisq for this #58 run is : 0.4502568425564806  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 59 fitting ...\n",
      "Elapse time : 62.07415986061096\n",
      "chisq for this #76 run is : 2.880616256381234  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 77 fitting ...\n",
      "Elapse time : 157.49791169166565\n",
      "chisq for this #72 run is : 0.3090742858289637  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 73 fitting ...\n",
      "Elapse time : 110.95228576660156\n",
      "chisq for this #68 run is : 0.3360456728850029  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 69 fitting ...\n",
      "Elapse time : 200.6650731563568\n",
      "chisq for this #59 run is : 0.5480311462418647  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 60 fitting ...\n",
      "Elapse time : 178.00090646743774\n",
      "chisq for this #77 run is : 0.5160538657387195  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 78 fitting ...\n",
      "Elapse time : 136.37968277931213\n",
      "chisq for this #69 run is : 0.3390674626448718  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 70 fitting ...\n",
      "Elapse time : 155.06031155586243\n",
      "chisq for this #73 run is : 0.3378168010748554  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 74 fitting ...\n",
      "Elapse time : 107.72531938552856\n",
      "chisq for this #60 run is : 0.4551149774650488  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 61 fitting ...\n",
      "Elapse time : 118.58368635177612\n",
      "chisq for this #70 run is : 0.3360456602878917  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 71 fitting ...\n",
      "Elapse time : 136.19154858589172\n",
      "chisq for this #74 run is : 0.30907428577346874  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 75 fitting ...\n",
      "Elapse time : 123.04646730422974\n",
      "chisq for this #61 run is : 0.320450640200063  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 62 fitting ...\n",
      "Elapse time : 236.4623622894287\n",
      "chisq for this #78 run is : 0.4244338433389111  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 79 fitting ...\n",
      "Elapse time : 121.47144627571106\n",
      "chisq for this #75 run is : 0.3378168030049634  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 76 fitting ...\n",
      "Elapse time : 170.1401183605194\n",
      "chisq for this #71 run is : 0.33906637203287315  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 72 fitting ...\n",
      "Elapse time : 109.11075830459595\n",
      "chisq for this #62 run is : 0.32045064010815016  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 63 fitting ...\n",
      "Elapse time : 142.5595302581787\n",
      "chisq for this #79 run is : 0.4244338392042551  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 80 fitting ...\n",
      "Elapse time : 85.03976845741272\n",
      "chisq for this #63 run is : 0.32045064037410115  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 64 fitting ...\n",
      "Elapse time : 62.15199017524719\n",
      "chisq for this #80 run is : 3.7340267305162946  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 81 fitting ...\n",
      "Elapse time : 221.64390754699707\n",
      "chisq for this #76 run is : 0.3378168026944977  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 77 fitting ...\n",
      "Elapse time : 109.75639247894287\n",
      "chisq for this #64 run is : 0.32045064010917695  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 65 fitting ...\n",
      "Elapse time : 215.77377796173096\n",
      "chisq for this #72 run is : 0.3390714656772659  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 73 fitting ...\n",
      "Elapse time : 71.733966588974\n",
      "chisq for this #65 run is : 0.9494669443381661  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 66 fitting ...\n",
      "Elapse time : 146.73131680488586\n",
      "chisq for this #73 run is : 0.3390667189425479  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 74 fitting ...\n",
      "Elapse time : 171.97578287124634\n",
      "chisq for this #77 run is : 0.3090742857588172  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 78 fitting ...\n",
      "Elapse time : 265.46344113349915\n",
      "chisq for this #81 run is : 0.42443384003891127  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 82 fitting ...\n",
      "Elapse time : 139.75944757461548\n",
      "chisq for this #66 run is : 0.32045064030487563  chisq best now is : 0.22358048735651032\n",
      "--------------------\n",
      "# 67 fitting ...\n",
      "Elapse time : 119.5566394329071\n",
      "chisq for this #78 run is : 1.2370820602098986  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 79 fitting ...\n",
      "Elapse time : 164.99345803260803\n",
      "chisq for this #82 run is : 0.30200702961034326  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 83 fitting ...\n",
      "Elapse time : 219.825847864151\n",
      "chisq for this #74 run is : 0.33907126193335707  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 75 fitting ...\n",
      "Elapse time : 117.59926724433899\n",
      "chisq for this #83 run is : 3.7307586951171694  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 84 fitting ...\n",
      "Elapse time : 154.4809651374817\n",
      "chisq for this #75 run is : 0.3390700553107971  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 76 fitting ...\n",
      "Elapse time : 249.42364358901978\n",
      "chisq for this #79 run is : 0.33563749119849934  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 80 fitting ...\n",
      "Elapse time : 371.1393964290619\n",
      "chisq for this #67 run is : 0.22358048735345132  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 68 fitting ...\n",
      "Elapse time : 89.04307413101196\n",
      "chisq for this #76 run is : 0.3360456469034765  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 77 fitting ...\n",
      "Elapse time : 128.264545917511\n",
      "chisq for this #80 run is : 0.33781680102487477  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 81 fitting ...\n",
      "Elapse time : 223.24714732170105\n",
      "chisq for this #84 run is : 3.7304706990122796  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 85 fitting ...\n",
      "Elapse time : 133.32796692848206\n",
      "chisq for this #77 run is : 0.33604566813864234  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 78 fitting ...\n",
      "Elapse time : 224.8016641139984\n",
      "chisq for this #68 run is : 0.22358048740233558  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 69 fitting ...\n",
      "Elapse time : 165.7362539768219\n",
      "chisq for this #81 run is : 0.3090742859755652  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 82 fitting ...\n",
      "Elapse time : 110.06048774719238\n",
      "chisq for this #78 run is : 0.33906420732573284  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 79 fitting ...\n",
      "Elapse time : 182.5038764476776\n",
      "chisq for this #85 run is : 0.3020070441646654  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 86 fitting ...\n",
      "Elapse time : 104.1421377658844\n",
      "chisq for this #69 run is : 0.45509246574727114  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 70 fitting ...\n",
      "Elapse time : 110.61676692962646\n",
      "chisq for this #82 run is : 0.33781680461952224  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 83 fitting ...\n",
      "Elapse time : 192.65250158309937\n",
      "Elapse time : 119.75726962089539\n",
      "chisq for this #79 run is : 0.33906427446345744  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 80 fitting ...\n",
      "chisq for this #83 run is : 0.3378168020514795  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 84 fitting ...\n",
      "Elapse time : 195.39918565750122\n",
      "chisq for this #86 run is : 0.3020070307548657  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 87 fitting ...\n",
      "Elapse time : 227.44135665893555\n",
      "chisq for this #70 run is : 0.22358048797504568  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 71 fitting ...\n",
      "Elapse time : 136.03217387199402\n",
      "chisq for this #84 run is : 0.3090742857275984  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 85 fitting ...\n",
      "Elapse time : 173.10004138946533\n",
      "chisq for this #80 run is : 0.3390694844340852  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 81 fitting ...\n",
      "Elapse time : 165.15129733085632\n",
      "chisq for this #87 run is : 0.42443383987063427  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 88 fitting ...\n",
      "Elapse time : 106.83025455474854\n",
      "chisq for this #88 run is : 0.3020070592594335  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 89 fitting ...\n",
      "Elapse time : 219.2021300792694\n",
      "chisq for this #71 run is : 0.4551149770940221  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 72 fitting ...\n",
      "Elapse time : 138.59878873825073\n",
      "chisq for this #81 run is : 0.3360456532604256  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 82 fitting ...\n",
      "Elapse time : 92.31274151802063\n",
      "chisq for this #72 run is : 0.3204506401868818  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 73 fitting ...\n",
      "Elapse time : 98.197110414505\n",
      "chisq for this #82 run is : 0.33906723957785573  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 83 fitting ...\n",
      "Elapse time : 286.0007092952728\n",
      "chisq for this #85 run is : 0.3356375131498432  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 86 fitting ...\n",
      "Elapse time : 208.2417652606964\n",
      "chisq for this #89 run is : 0.3020070327448044  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 90 fitting ...\n",
      "Elapse time : 142.06156492233276\n",
      "chisq for this #83 run is : 0.3390665484568559  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 84 fitting ...\n",
      "Elapse time : 264.5685758590698\n",
      "chisq for this #73 run is : 0.3204506401678834  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 74 fitting ...\n",
      "Elapse time : 122.89284706115723\n",
      "chisq for this #84 run is : 0.3390557352516492  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 85 fitting ...\n",
      "Elapse time : 287.7719385623932\n",
      "chisq for this #86 run is : 0.33563751445248097  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 87 fitting ...\n",
      "Elapse time : 234.88898468017578\n",
      "chisq for this #90 run is : 0.3020070335785639  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 91 fitting ...\n",
      "Elapse time : 115.28438806533813\n",
      "chisq for this #74 run is : 0.3204506404421272  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 75 fitting ...\n",
      "Elapse time : 115.82691836357117\n",
      "chisq for this #85 run is : 0.33604564591970076  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 86 fitting ...\n",
      "Elapse time : 94.57957863807678\n",
      "chisq for this #91 run is : 0.4381742029189011  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 92 fitting ...\n",
      "Elapse time : 172.87221884727478\n",
      "chisq for this #87 run is : 2.0198942392908714  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 88 fitting ...\n",
      "Elapse time : 123.38158655166626\n",
      "chisq for this #86 run is : 0.33604566310599915  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 87 fitting ...\n",
      "Elapse time : 98.18376803398132\n",
      "chisq for this #92 run is : 0.4381742030499826  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 93 fitting ...\n",
      "Elapse time : 87.0168879032135\n",
      "chisq for this #93 run is : 2.034744041792587  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 94 fitting ...\n",
      "Elapse time : 130.8135313987732\n",
      "chisq for this #88 run is : 0.33781680107209844  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 89 fitting ...\n",
      "Elapse time : 291.93625950813293\n",
      "chisq for this #75 run is : 0.22358048737558212  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 76 fitting ...\n",
      "Elapse time : 72.94579124450684\n",
      "chisq for this #94 run is : 3.7340267300790737  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 95 fitting ...\n",
      "Elapse time : 232.7331359386444\n",
      "chisq for this #87 run is : 0.33906861496410823  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 88 fitting ...\n",
      "Elapse time : 83.58953261375427\n",
      "chisq for this #76 run is : 0.32764672925221167  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 77 fitting ...\n",
      "Elapse time : 119.74586534500122\n",
      "chisq for this #95 run is : 0.4381742033631105  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 96 fitting ...\n",
      "Elapse time : 219.91826105117798\n",
      "chisq for this #89 run is : 0.3356375559708174  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 90 fitting ...\n",
      "Elapse time : 114.49440264701843\n",
      "chisq for this #88 run is : 0.3390684981159943  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 89 fitting ...\n",
      "Elapse time : 108.69086575508118\n",
      "chisq for this #77 run is : 0.3204506403189721  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 78 fitting ...\n",
      "Elapse time : 119.59488010406494\n",
      "chisq for this #96 run is : 0.43817420292939685  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 97 fitting ...\n",
      "Elapse time : 95.34701633453369\n",
      "chisq for this #89 run is : 0.33906850977334513  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 90 fitting ...\n",
      "Elapse time : 74.80115413665771\n",
      "chisq for this #97 run is : 0.9410327468288343  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 98 fitting ...\n",
      "Elapse time : 160.53409504890442\n",
      "chisq for this #90 run is : 0.30907428599786546  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 91 fitting ...\n",
      "Elapse time : 185.65095806121826\n",
      "chisq for this #78 run is : 0.22358048740654068  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 79 fitting ...\n",
      "Elapse time : 118.84905552864075\n",
      "chisq for this #91 run is : 1.3593353989482284  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 92 fitting ...\n",
      "Elapse time : 158.9169135093689\n",
      "chisq for this #98 run is : 0.42443383968735515  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "# 99 fitting ...\n",
      "Elapse time : 281.58523869514465\n",
      "chisq for this #90 run is : 0.333471347681134  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 91 fitting ...\n",
      "Elapse time : 239.93797707557678\n",
      "chisq for this #79 run is : 0.22512452668421776  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 80 fitting ...\n",
      "Elapse time : 141.6446270942688\n",
      "chisq for this #99 run is : 0.42443384215944957  chisq best now is : 0.30200702934260026\n",
      "--------------------\n",
      "Finally, best parameters are :------\n",
      " [9.22751999e-01 3.95782971e-04 4.63373540e+00 5.70252287e+01\n",
      " 3.98842373e-01 1.05604574e+01 1.80134030e+01 4.46453908e-01\n",
      " 2.87632747e+01 5.20045611e+00 8.00000000e-01 4.43860288e+01]\n",
      "Did save the params to : ./results1\\R-PEG5k_AgNP10_2mM_PAA_HCl-3d4824d3_params.csv\n",
      "Saved experimental data to: ./results1\\R-PEG5k_AgNP10_2mM_PAA_HCl-3d4824d3_rrf.csv\n",
      "Saved fit data to: ./results1\\R-PEG5k_AgNP10_2mM_PAA_HCl-3d4824d3_rrf_fit.csv\n",
      "Saved electron density data to: ./results1\\R-PEG5k_AgNP10_2mM_PAA_HCl-3d4824d3_ED.csv\n",
      "Elapse time : 184.15274214744568\n",
      "chisq for this #92 run is : 0.3090742859909111  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 93 fitting ...\n",
      "Elapse time : 89.60278582572937\n",
      "chisq for this #91 run is : 0.3360456602290168  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 92 fitting ...\n",
      "Elapse time : 67.0356957912445\n",
      "chisq for this #80 run is : 0.32045064013831226  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 81 fitting ...\n",
      "Saved the figure to: ./results1\\R-PEG5k_AgNP10_2mM_PAA_HCl-3d4824d3_combined_fig.png\n",
      "Elapse time : 94.82103991508484\n",
      "chisq for this #92 run is : 0.3360456468139779  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 93 fitting ...\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 487,
       "width": 988
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_4 is done\n",
      "Elapse time : 82.19111061096191\n",
      "chisq for this #81 run is : 0.3204506401448039  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 82 fitting ...\n",
      "Elapse time : 145.94058895111084\n",
      "chisq for this #93 run is : 0.3090742859833095  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 94 fitting ...\n",
      "Elapse time : 39.1567747592926\n",
      "chisq for this #94 run is : 1.833777177210743  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 95 fitting ...\n",
      "Elapse time : 70.54059624671936\n",
      "chisq for this #82 run is : 0.45511495959497994  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 83 fitting ...\n",
      "Elapse time : 70.53946328163147\n",
      "chisq for this #83 run is : 0.32045064022994585  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 84 fitting ...\n",
      "Elapse time : 126.34909081459045\n",
      "chisq for this #95 run is : 0.33781680094540434  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 96 fitting ...\n",
      "Elapse time : 192.69641590118408\n",
      "chisq for this #93 run is : 0.3334713465005357  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 94 fitting ...\n",
      "Elapse time : 107.74737095832825\n",
      "chisq for this #84 run is : 0.3204506401199497  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 85 fitting ...\n",
      "Elapse time : 105.79840779304504\n",
      "chisq for this #94 run is : 0.3360456462814228  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 95 fitting ...\n",
      "Elapse time : 122.61038899421692\n",
      "chisq for this #96 run is : 0.30907428592889524  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 97 fitting ...\n",
      "Elapse time : 99.18078637123108\n",
      "chisq for this #85 run is : 0.40467899092618403  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 86 fitting ...\n",
      "Elapse time : 78.95855903625488\n",
      "chisq for this #97 run is : 0.33781680139946196  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 98 fitting ...\n",
      "Elapse time : 139.06722497940063\n",
      "chisq for this #95 run is : 0.33907032884382515  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 96 fitting ...\n",
      "Elapse time : 73.7108907699585\n",
      "chisq for this #86 run is : 0.32045064044941923  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 87 fitting ...\n",
      "Elapse time : 80.04562544822693\n",
      "chisq for this #87 run is : 0.32045064040254273  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 88 fitting ...\n",
      "Elapse time : 143.03216576576233\n",
      "chisq for this #98 run is : 0.3378168012223655  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "# 99 fitting ...\n",
      "Elapse time : 107.27636861801147\n",
      "chisq for this #96 run is : 0.3360456671285682  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 97 fitting ...\n",
      "Elapse time : 95.15785193443298\n",
      "chisq for this #99 run is : 0.33781680215627546  chisq best now is : 0.3090742857262205\n",
      "--------------------\n",
      "Finally, best parameters are :------\n",
      " [9.14962605e-01 7.39103790e-04 4.75872629e+00 5.77365439e+01\n",
      " 3.87481559e-01 1.27199031e+01 7.00000000e+01 3.99752748e-01\n",
      " 3.42132174e+01 5.00000001e+00 7.19056296e-01 4.43688961e+01]\n",
      "Did save the params to : ./results1\\R-PEG40k_AgNP10_2mM_PAA_HCl-10b2d544_params.csv\n",
      "Saved experimental data to: ./results1\\R-PEG40k_AgNP10_2mM_PAA_HCl-10b2d544_rrf.csv\n",
      "Saved fit data to: ./results1\\R-PEG40k_AgNP10_2mM_PAA_HCl-10b2d544_rrf_fit.csv\n",
      "Saved electron density data to: ./results1\\R-PEG40k_AgNP10_2mM_PAA_HCl-10b2d544_ED.csv\n",
      "Elapse time : 155.55363726615906\n",
      "chisq for this #88 run is : 0.2235804891235698  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 89 fitting ...\n",
      "Saved the figure to: ./results1\\R-PEG40k_AgNP10_2mM_PAA_HCl-10b2d544_combined_fig.png\n",
      "Elapse time : 139.79721474647522\n",
      "chisq for this #97 run is : 0.6057997913057788  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 98 fitting ...\n",
      "Elapse time : 35.91004753112793\n",
      "chisq for this #98 run is : 0.6111449655066307  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "# 99 fitting ...\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 487,
       "width": 988
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_1 is done\n",
      "Elapse time : 50.23280572891235\n",
      "chisq for this #89 run is : 0.32045064097072573  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 90 fitting ...\n",
      "Elapse time : 62.766987800598145\n",
      "chisq for this #90 run is : 0.3204506403107236  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 91 fitting ...\n",
      "Elapse time : 32.21078038215637\n",
      "chisq for this #91 run is : 0.4551158295788933  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 92 fitting ...\n",
      "Elapse time : 121.28739905357361\n",
      "chisq for this #99 run is : 0.6057997960321689  chisq best now is : 0.3334713463309767\n",
      "--------------------\n",
      "Finally, best parameters are :------\n",
      " [9.18271697e-01 6.88708944e-04 9.95292747e+00 1.28299655e+01\n",
      " 5.64057175e-01 2.67218062e+00 7.00000000e+01 3.82389658e-01\n",
      " 2.64711419e+01 5.00000000e+00 4.27775630e-01 5.00000000e+01]\n",
      "Did save the params to : ./results1\\R-PEG20k_AgNP10_2mM_PAA_HCl-6adee353_params.csv\n",
      "Saved experimental data to: ./results1\\R-PEG20k_AgNP10_2mM_PAA_HCl-6adee353_rrf.csv\n",
      "Saved fit data to: ./results1\\R-PEG20k_AgNP10_2mM_PAA_HCl-6adee353_rrf_fit.csv\n",
      "Saved electron density data to: ./results1\\R-PEG20k_AgNP10_2mM_PAA_HCl-6adee353_ED.csv\n",
      "Saved the figure to: ./results1\\R-PEG20k_AgNP10_2mM_PAA_HCl-6adee353_combined_fig.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 487,
       "width": 988
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_6 is done\n",
      "Elapse time : 64.2343807220459\n",
      "chisq for this #92 run is : 0.3204506403061014  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 93 fitting ...\n",
      "Elapse time : 31.424928426742554\n",
      "chisq for this #93 run is : 0.45511486562542647  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 94 fitting ...\n",
      "Elapse time : 20.22090458869934\n",
      "chisq for this #94 run is : 0.3204506401313799  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 95 fitting ...\n",
      "Elapse time : 65.08077621459961\n",
      "chisq for this #95 run is : 0.22358048792354965  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 96 fitting ...\n",
      "Elapse time : 25.528733015060425\n",
      "chisq for this #96 run is : 0.45511394744233785  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 97 fitting ...\n",
      "Elapse time : 22.978147506713867\n",
      "chisq for this #97 run is : 0.32045064015887154  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 98 fitting ...\n",
      "Elapse time : 18.11680841445923\n",
      "chisq for this #98 run is : 0.4551270086722001  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "# 99 fitting ...\n",
      "Elapse time : 38.4931378364563\n",
      "chisq for this #99 run is : 0.4551150960468416  chisq best now is : 0.22358048735345132\n",
      "--------------------\n",
      "Finally, best parameters are :------\n",
      " [9.21493132e-01 6.22180305e-04 4.00697617e+00 1.81811045e+01\n",
      " 3.68232978e-01 1.96916136e+00 5.99581187e+01 3.34409360e-01\n",
      " 3.19730179e+01 5.00000000e+00 7.61471945e-01 2.95392145e+01]\n",
      "Did save the params to : ./results1\\R-PEG2k_AgNP10_2mM_PAA_HCl-36e34205_params.csv\n",
      "Saved experimental data to: ./results1\\R-PEG2k_AgNP10_2mM_PAA_HCl-36e34205_rrf.csv\n",
      "Saved fit data to: ./results1\\R-PEG2k_AgNP10_2mM_PAA_HCl-36e34205_rrf_fit.csv\n",
      "Saved electron density data to: ./results1\\R-PEG2k_AgNP10_2mM_PAA_HCl-36e34205_ED.csv\n",
      "Saved the figure to: ./results1\\R-PEG2k_AgNP10_2mM_PAA_HCl-36e34205_combined_fig.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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5558d7nfjiZdFHaOcnBxORJLzMUpJSdGZM2ckle7/f0nGyF47cI7jyLtwDHmXw4cP63e/+50qVqyo0NBQxcbGatCgQfrwww8dnqzhqnFEQa74nIbS+/TTT9WiRQuFh4crKipKjRs31ujRox3OlvfX1yQS3CixG5fvCg8PL7TejduKuuRXadvOv+9o3xu3l7elyLx57CTplltu0bvvvquTJ08qMzNTV65cUVJSkpYvX66GDRsqKytLTzzxhJYvX16kmPyJO8eutDjuHPPmsZM47gpTVuOWnp6uoUOHKi0tTVWqVNHbb7/tNbH5Cm8eO0lq3LixpkyZori4OGVkZOjy5ctKTU3VmjVr1L59exmGoTfffFPTp08vdUy+xlNjZzabVa1aNVWrVq3AkvJDhw7VlClTVKlSpUJj473OPm8eO4n3OsBTTp48aT0JJTg4WHXr1tWIESO0Z88eSVK/fv3svr9lZGQoMTFRkgpdajdfTEyMIiIiJMn6A3a+s2fPWu87a6d27drW+79tpzyaOnWqcnJyJMm6ZKsjP//8s44dO6bMzEwlJSXp4MGDmj17ttq3b6/HHnvMOtP4txijknM2Rq56bhkjz+E48i4cQ97lwoUL2rFjh65fv67MzEzFx8dr1apVGj16tNq2bVto8o3n3/Vc9TkNpXfw4EEdOnRI6enpSklJ0dGjR/Xhhx/qjjvu0ODBg3X9+nWbffz1mCDBDcDrjB8/Xk8++aTq1q1rXXIrPDxcQ4YM0fbt21W/fn1J0oQJE7z+OhCAr+C48x45OTm6//77tWvXLgUFBWnRokWqWbNmWYeFIijO2D3wwAN64YUX1KRJEwUFBUmyLB/Yt29fbd68WR07dpQkTZw40e6XE5Re1apVdf78eZ0/f15paWk6c+aMXnnlFX3xxRe6+eabNWfOnLIOEYUo6djxXgeUrSpVqmjZsmVavXq13dVNbjzhJTIy0ml7+T+c/naFmuK0k9+GvXbKm+3bt+udd96RZPnh8/HHHy+0bsWKFTV27FgtXLhQW7Zs0c8//6zVq1frmWeesT7ns2fP1oMPPmh3f8aoZIoyRq56bhkj9+M48k4cQ94hICBAvXr10vTp07V27Vrt3r1bP/zwg9555x3r6hUHDx5Uz549dfr0aZv9ef5dz1Wf01By4eHhGj58uP7zn/9o06ZN2r17t7799lu98sorqly5siTLddIHDRpks8KBvx4TJLhRYjcuJ5aWllZovRu32VuCzB1t5993tO+N24sal7/w5rFzpnLlynr55ZclSadOnSp31yh19/NbGhx3jnnz2DlTno87T49bbm6uHnjgAa1cuVKBgYFavHix+vbt6xWx+RpvHjtnQkND9dZbb0myfJlYt25diePyRWXxf9tkMik2NlaTJk3SokWLlJ2drccff1x79+61GxvvdfZ589g5U57f6wBXq1Wrlvbt26d9+/Zp9+7d+vLLL/XUU08pNTVVjz32mKZMmWJ3v4yMDOt9R9fNzhcSEiLJsnpKSdvJb8NeO+XJhQsX9Ic//EE5OTkymUxauHBhoStx1KxZU/Hx8Zo/f75GjRqlzp07q127drrrrrv0zjvv6Oeff1adOnUkSYsXL9aqVats2mCMiq+oY+Sq55Yxci+OI+/FMeQd/vvf/2rt2rX605/+pF69eqlt27bq1q2bnnnmGe3du1ejR4+WZHltfPbZZ2325/l3PVd9TkPJxcfHa8mSJXrooYfUtWtXtW3bVn369NGkSZN04MABtWvXTpK0ceNGm0tv+esxQYIbJXbjjKT4+PhC6924ragz0IrbdnR0dIEzT/L3v3r1qsODMH//8jYzzpvHrig6d+5svX/8+PFi7evr3Dl2pcVx55g3j11RlNfjzpPjlpubq5EjR+qTTz6R2WzWxx9/rD/84Q8ui60kr7e+zJvHrijK6zEnlf3r5ZAhQ1SnTh3l5eVp3rx5dmPjvc4+bx67oijPxx3Kj/ylw0tz++CDDxz2ERQUpFatWqlVq1Zq27atBgwYoFmzZmnbtm0ymUx6+eWX7c5IvPGSA1lZWU4fS/6yvWFhYSVu58alf3/bTlnxxBjdKDk5WQMGDLAuXfn222/rjjvuKLR+cHCww8tQNG7cWB9//LH171mzZtnUYYzcN0auem4Zo+KNUXGV9+PIm8eHY6jo3DmOFStWLLTfoKAgzZ07V02bNpUkrVixwua7R3l4/j3NVZ/TUHKOjotq1arps88+s64Q+Nv3DX89Jkhwo8SaN2+ugADLf6H9+/cXWi9/W/Xq1Qu9Ht5vtWrVymZ/R223aNGiVPu3bNmySHH5C28eOzjmzrErLY47x7x57FA4T41b/uzfpUuXWhOkw4YNc7gPr7eOefPYwTFveL2sVauWJOno0aMFynmvc8ybxw5A2bv55ps1adIkSdKCBQv07bffFth+44oORVkOMTU1VZLtMovFaSe/DXvtlAcZGRkaNGiQdu3aJUl6/vnn9ec//7nU7Xbr1s362XPz5s3Ky8srsJ0xKrrijpGrnlvGqOxxHJUNjiHfEBgYqHHjxln/3rhxY4HtPP+u56rPaXCfBg0aqE+fPpIs34cTEhKs2/z1mAgs6wDgu8LDw9WlSxdt2rRJ33zzjV544QWbOoZhaM2aNZJUrGU6mzRpojp16uj06dP65ptvNHToUJs6qamp2rRpk922u3btqrCwMKWnp+ubb76xXsfyRqdOndKhQ4eKHZs/8OaxK4pt27ZZ7+dfK7G8cOfYlRbHnWPePHZFUV6PO0+MW25uru6///4Cs3+HDx/udD9PvN76Mm8eu6Ior8ecVPavl4Zh6MSJE5Jsl8/mvc4xbx67oijPxx3Kj/zXp9KoUaNGifcdNGiQnnjiCUnSZ599VuB1IDQ0VJUrV9bly5etM1ULc/XqVeuPb7Vr1y6wLTY21nrfWTtnzpyx3v9tO2XFU2OUk5Oj++67T+vXr5ckPfTQQ5o6dWqp+87XokULHTx4UBkZGbp8+bKqVq1q3fbbMerQoUOh7TBGxRuj/BO9pNL9/+c4Kt1rnav463HkzePDMVR0ZT2ON57E/9sZ3K4aR/yPqz6nwb1atGihr776SpLluMhfMc1vX5MMoBTmzp1rSDJMJpOxbds2m+3Lli0zJBmSjLVr1xar7VdffdWQZISHhxsnTpyw2T558mRDkmE2m424uDib7SNHjjQkGTVq1DCuXbtms/3xxx83JBlRUVHGlStXihWbP/DWscvLy3PY9uXLl40GDRoYkozatWsbubm5xYrNH7hz7OzJb2vBggVO63LcOeatY8dx55g7xy0nJ8cYNmyYIckIDAw0li5dWqz9S/te6e+8deycHXMZGRnG7373O0OSERERYVy9erVYsfkDd41ddna20zrz5s2ztv2vf/3LZjvvdY5569jxXgd4h6ysLOtx2rdvX5vt3bp1s77/OTrut2zZYm3ntddeK7AtMzPTMJvNhiSjX79+DuN56623rO18//33JXtQPig3N9cYPny49bEPGzbM5a97Q4cOtbZ/8eLFAtvWrVtn3fb3v//dYTt9+/a1ft7KyspyaYzerDRjVLt2bUOS0bRpU4f1Fi9ebG1//vz5BbYdO3bMuu3RRx912M4jjzxirXv8+PEixehv6tata0gy6tat69J2OY5co7jjwzHkG1avXm193qZMmWKz3RXjiIJc8TkN7vXCCy9Yn/sdO3ZYy/31NYkEN0olOzvbaN26tSHJqFWrlvVHqtzcXOOTTz4xoqOjDUlG//79bfZ9/fXXrQeKvR/lr127ZlSvXt2QZLRo0cLYuXOnYRiWL6v/+te/jODgYEOS8fjjj9uN7fjx40ZERIQhyejWrZtx5MgRwzAMIyUlxXjjjTcMk8lkSDImT57somfDt3jr2H344YfG4MGDjc8++8y4cOGCtTwtLc1YsWKF0aRJE2vfxU0E+Qt3jp1hGMalS5cK3PLrz5o1q0B5amqqzb4cd45569hx3DnmrnHLycmx/mgVGBhofPLJJ8WOrbTvlf7OW8duw4YNRq9evYwPP/zQOHPmjLU8KyvLWLt2rdGxY0dr37xeunbs1q9fb3Tr1s3muTcMwzhy5Ijxl7/8xQgMDDQkGQ0bNjTS0tJs2ue9zjFvHTve6wDvcOLECeuxNmTIEJvtL730knW7vZNk8v3973+31luzZo3N9s6dOxuSjOjoaCMzM7PQdvr162dIMkJCQoykpKSSPSgf9NBDD1mfv4EDBxbpJKLiatmypfW5/W1iNikpyfo59c477yy0jczMTOv7RufOnV0eozcrzRiNGDHCuu+5c+cKrffoo49a69mbfFCzZk1DktGsWTOH/TVr1sz6vu/shDJ/5a4EN8eRaxR3fDiGfMPUqVOtz//HH39ss90V44iCXPU5De4zYMAA63N/9uxZa7m/viaR4EapnThxwqhXr571wAkPDzdCQ0Otf7dr187u7JWiJGt27txpVK5c2VovKirKCAoKsv7dt29fIyMjo9DYVq9ebYSHh1vrV6hQwXomtyRj7NixXn+QupM3jt2CBQusdSTLGWGVK1cuMG4hISHGe++95+qnw6e4c+xufP4d3V5//XW7+3PcOeaNY8dx55w7xm3jxo3WbUFBQUa1atUc3gpLuJT2vdLfeePYrV+/vsAxFxYWZlSpUqXAuAUEBBgvv/yyO58ar+eOsfvtcx8aGmpUqVLFCAsLK1Depk2bQl9rDYP3Ome8cex4rwO8w5QpUxx+n9i+fbt1e2GzS3Jzc43mzZsbkoyKFSvanY2Yv4qNJGPJkiV22zlz5oz1NeCuu+4q1ePyJc8995z1uenVq5dbPidu3ry5QB/29O/f35AsJwv+9sSlfEuWLLG2Y292nr8q7RjduFpKYTN7U1NTjZiYGEOynChrT/6qNJKMrVu32q2zdetWa50nnniiWHH6E3ckuDmOXKe448Mx5P2ys7OtnwUkGadPn7ap46pxxP+46nMa3OP48ePWE58aNmxos90fX5N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      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 487,
       "width": 988
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_2 is done\n"
     ]
    }
   ],
   "source": [
    "# Define your function that will be executed in parallel\n",
    "def process_condition(cond):\n",
    "    try:\n",
    "        filename = filenames[cond]\n",
    "        data_path = os.path.join(path, filename)\n",
    "        monte_carlo_process(data_path, output_dir, filename, 100)\n",
    "        print(f\"{cond} is done\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error processing {cond}: {e}\")\n",
    "\n",
    "# Use ThreadPoolExecutor to parallelize the processing\n",
    "with ThreadPoolExecutor() as executor:\n",
    "    executor.map(process_condition, conds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c6feb69-f929-457a-962c-713cf8f3d1e8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5dc38aeb-5ef3-4461-ad18-bbb4032b3092",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "## Additional plotting code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e87c4fb2-940e-47a1-a27a-e21e6755a987",
   "metadata": {},
   "outputs": [],
   "source": [
    "# popt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "996c3b0e-91f8-4845-a764-bf1309037936",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv( os.path.join(path_1,'results1', 'T2-PEG2k-Ag10-1to1-PEG5k-Au5-K2CO3-100mM-scan638-641_ref' + '_params.csv'))\n",
    "# df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94217ad3-997a-42aa-adff-d81d0908773f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pbest = df['params'][0]\n",
    "pbest\n",
    "# Remove the newline characters and split the string into individual elements\n",
    "pbest = pbest.replace('\\n', '').replace('[', '').replace(']', '').split()\n",
    "\n",
    "# Convert the elements into a NumPy array of floats\n",
    "pbest = np.array(pbest, dtype=float)\n",
    "pbest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56be1c2e-3a1d-4b26-b550-f6187ea3153e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(path+'T2-PEG2k-Ag10-1to1-PEG5k-Au5-K2CO3-100mM-scan638-641_ref.txt', sep='\\s+',names=['qz','ref','error'])  \n",
    "x = data['qz'][0:]\n",
    "y = data['ref'][0:]\n",
    "yerr = data['error'][0:]\n",
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57b1db29",
   "metadata": {},
   "outputs": [],
   "source": [
    "xfit=np.linspace(np.min(x),np.max(x),200)\n",
    "yfit=fitRef(xfit, *pbest) \n",
    "\n",
    "fig, ax =plt.subplots(nrows=1, ncols=2, figsize=(18,8));\n",
    "ax[0].errorbar(x, y, yerr=yerr,fmt='o',markersize=10,\n",
    "              markerfacecolor='w',alpha=0.8);\n",
    "ax[0].plot(xfit, yfit, '-', color='r', \n",
    "           lw=3,label='data');\n",
    "ax[0].set_yscale('log')\n",
    "ax[0].set_ylabel('R',fontsize=20)\n",
    "ax[0].set_xlabel('Qz [1/A]', fontsize=20)\n",
    "\n",
    "z=np.arange(-300,50,1)\n",
    "rho=fitRho(z, *pbest)\n",
    "ax[1].plot(z, rho,'-',lw=3, color='k');\n",
    "ax[1].set_ylim(-0.05,0.6)\n",
    "ax[1].set_ylabel(r'$\\rho$ [e/A3]',fontsize=20)\n",
    "ax[1].set_xlabel('z [A]',fontsize=20)\n",
    "# p=popt.copy()\n",
    "# p[2::3]=0.0001\n",
    "rho_rect = fitRho(z, *pbest)\n",
    "# ax[1].plot(z, rho_rect,'--',lw=3, color='b');\n",
    "# ax[1].set_ylim(-0.05,0.6)\n",
    "# ax[1].set_ylabel(r'$\\rho$ [e/A3]',fontsize=20)\n",
    "# ax[1].set_xlabel('z [A]',fontsize=20)\n",
    "#do not need boxes for figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bfd2048-fa15-4acd-b65b-acbf6c35126b",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cb3c553-e215-4589-a98f-59fc8eeaad97",
   "metadata": {},
   "outputs": [],
   "source": [
    "xfit = np.linspace(np.min(x), np.max(x), 200)\n",
    "popt = pbest\n",
    "xerr = popt[1]\n",
    "y_RF = fitRF(x - xerr)\n",
    "y_RF_fit = fitRF(xfit - xerr)\n",
    "yfit = fitRef(xfit, *popt) / y_RF_fit\n",
    "\n",
    "yn = y / y_RF\n",
    "yn_err = yerr / y_RF\n",
    "\n",
    "# Create a DataFrame\n",
    "df = pd.DataFrame({\n",
    "    'x': x,\n",
    "    'yn': yn\n",
    "})\n",
    "\n",
    "df2 = pd.DataFrame({\n",
    "    'xfit': xfit,\n",
    "    'yfit': yfit\n",
    "})\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4, 4))\n",
    "ax.plot(x, yn, 'o', markersize=8, label='Experimental Data', color='red', alpha=0.6)\n",
    "ax.plot(xfit, yfit, '-', lw=3, label='Fit')\n",
    "\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel(r'$Q_z$', fontsize=10)\n",
    "ax.set_ylabel(r'$xR/RF$', fontsize=10)\n",
    "ax.tick_params(axis='both', labelsize=9)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f37c7319-4eed-4a11-8012-d17a9bd2303c",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = popt\n",
    "p[4::3] = 0.334\n",
    "rhoRef = fitRho(z, *p)\n",
    "rr = np.sum(rho - rhoRef)\n",
    "\n",
    "# Create a DataFrame from z and rhoRef\n",
    "df = pd.DataFrame({'z': z, 'rho': rho})\n",
    "\n",
    "plt.plot(z, rhoRef,'-',lw=3, color='k');\n",
    "plt.plot(z, rho, '-', lw=3, color='red')\n",
    "plt.ylim(-0.05,0.6)\n",
    "plt.ylabel(r'$\\rho$ [e/A3]',fontsize=20)\n",
    "plt.xlabel('z [A]',fontsize=20)\n",
    "print(rr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c66db52f-2bf4-4a9b-934f-3d76fbb91b30",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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